Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model

TL;DR

This paper introduces the CAS model, a comprehensive user behavior model for search engine results pages that jointly considers clicks, attention, and satisfaction, leading to more accurate evaluation metrics.

Contribution

The paper presents a novel CAS model that captures user attention and satisfaction on non-linear SERPs, improving upon click-based evaluation methods.

Findings

CAS model predicts user actions and satisfaction more accurately.

New evaluation metric aligns better with user satisfaction.

Model accounts for non-linear SERP layouts and direct utility.

Abstract

Modern search engine result pages often provide immediate value to users and organize information in such a way that it is easy to navigate. The core ranking function contributes to this and so do result snippets, smart organization of result blocks and extensive use of one-box answers or side panels. While they are useful to the user and help search engines to stand out, such features present two big challenges for evaluation. First, the presence of such elements on a search engine result page (SERP) may lead to the absence of clicks, which is, however, not related to dissatisfaction, so-called "good abandonments." Second, the non-linear layout and visual difference of SERP items may lead to non-trivial patterns of user attention, which is not captured by existing evaluation metrics. In this paper we propose a model of user behavior on a SERP that jointly captures click behavior, user attention and satisfaction, the CAS model, and demonstrate that it gives more accurate predictions of user actions and self-reported satisfaction than existing models based on clicks alone. We use the CAS model to build a novel evaluation metric that can be applied to non-linear SERP layouts and that can account for the utility that users obtain directly on a SERP. We demonstrate that this metric shows better agreement with user-reported satisfaction than conventional evaluation metrics.